Factors Associated with Low Back Pain among Nursing Personnel
Bibliographic record
Abstract
Introduction: Globally low back pain has been found to affect the quality of peoples’ health hence affecting work output. Higher prevalence of low back pain is reported among nurses which is neglected and responsible for serious suffering and disability and thus affecting quality of patient care. Methods: A descriptive cross-sectional study was conducted at National Medical College and Teaching Hospital Birganj, Nepal among 101 nurses. Ethical approval was obtained from Institutional Review Committee, written informed consent was taken and Semi-structured questionnaire was distributed to the participants for data collection. Data was analyzed by descriptive and inferential statistics. Results: Out of 101 nursing personnel, 79 (78.2%) had experienced low back pain. Half of them 50 (49.5%) had mild pain, 24 (23.8%) had moderate pain whereas only 5 (5%) had severe type of pain. Quebec disability score ranged from 3 to 81 with mean score 25.96 ± 16.41. Majority of the nurses 69 (87.34%) who experienced low back pain did not seek treatment. There was significant association of low back pain and gender(P=.019), performed household work on their own (P=.050) and work experience (P=.007). There was significant association of low back pain with stressful work environment (P=.000) and overtime duties (P=.005). Conclusion: Low back pain is common among nursing personnel, even though most of the nurses did not seek treatment for low back pain which is worrisome and calls for urgent attention to maintain optimal health of these frontline health workers. The factors related to low back pain among nursing professionals is multi-factorial. Key words: Factors, Low Back Pain, Nurses, Quebec disability scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".